In our ULAST, we use the unsupervised trained optical flow model [1]} to generate the pseudo labels of the first frame, which will be used in the training process. However, these pseudo labels often are noisy, which hinders the performance of the trained tracker.
Existing approaches [2]}, [3]} only use the classification confidence from the response map to filter out label noise. We argue that the classification result is not sufficient to evaluate the importance of each sample. Therefore, we also take the regression result into consideration.
Intuitively, template kernels generated from pseudo labels with more noise often produce larger response area from background in search regions, which can be identified from Fig REF .
To this end, we propose a mask-guided loss re-weighting strategy to re-weight the loss from the pseudo-labels of various qualities.
Specifically, suppose we have a video batch of size \(B\) . For the \(b\) -th sample in this batch, we first use the tracker to generate the classification output \(P_{cls}\)  and regression output \(P_{reg}\)  by constructing a template-search pair from the first frame. Then, based on \(P_{cls}\)  and \(P_{reg}\) , we get the regional mask \(\hat{M}^b\)  by using Eq. REF , REF  and REF  for this video.
On the other hand, given the pseudo-labelled first frame, we also calculate \(\bar{M}^{b}\) , the only difference is that we set \(K=1\)  and \(s_k=1\)  in Eq. REF , as there is only one ground-truth box in the first frame in this case.
Then we learn a dynamic weight \(w_b\)  for each sample in a batch according to the relatively high response region in such formulation:
\(w_b =\log _{\gamma }(\alpha - \frac{\sum _{p}{\sum _{q}\mathbb {1}(\hat{M}^b_{p, q}, \beta )}}{\sum _{p}\sum _{q}\mathbb {1}(\bar{M}^{b}_{p,q}, \beta )})),\) 
